# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest

import numpy as np
from op_test import OpTest

import paddle
from paddle import base


class TestRepeatInterleaveOp(OpTest):
    def setUp(self):
        self.op_type = "repeat_interleave"
        self.python_api = paddle.repeat_interleave
        self.init_dtype_type()
        index_np = np.random.randint(
            low=0, high=3, size=self.index_size
        ).astype(self.index_type)
        x_np = np.random.random(self.x_shape).astype(self.x_type)

        self.inputs = {'X': x_np, 'RepeatsTensor': index_np}
        self.attrs = {'dim': self.dim, 'output_size': -1}

        outer_loop = np.prod(self.x_shape[: self.dim])
        x_reshape = [outer_loop, *self.x_shape[self.dim :]]
        x_np_reshape = np.reshape(x_np, tuple(x_reshape))
        out_list = []
        for i in range(outer_loop):
            for j in range(self.index_size):
                for k in range(index_np[j]):
                    out_list.append(x_np_reshape[i, j])
        self.out_shape = list(self.x_shape)
        self.out_shape[self.dim] = np.sum(index_np)
        self.out_shape = tuple(self.out_shape)

        out = np.reshape(out_list, self.out_shape)
        self.outputs = {'Out': out}

    def init_dtype_type(self):
        self.dim = 1
        self.x_type = np.float64
        self.index_type = np.int64
        self.x_shape = (8, 4, 5)
        self.index_size = self.x_shape[self.dim]

    def test_check_output(self):
        self.check_output(check_pir=True, check_symbol_infer=False)

    def test_check_grad_normal(self):
        self.check_grad(['X'], 'Out', check_pir=True)


class TestRepeatInterleaveOp2(OpTest):
    def setUp(self):
        self.op_type = "repeat_interleave"
        self.python_api = paddle.repeat_interleave
        self.init_dtype_type()
        index_np = 2
        x_np = np.random.random(self.x_shape).astype(self.x_type)
        self.inputs = {'X': x_np}  # , 'RepeatsTensor': None}
        self.attrs = {'dim': self.dim, 'Repeats': index_np, 'output_size': -1}

        outer_loop = np.prod(self.x_shape[: self.dim])
        x_reshape = [outer_loop, *self.x_shape[self.dim :]]
        x_np_reshape = np.reshape(x_np, tuple(x_reshape))
        out_list = []
        for i in range(outer_loop):
            for j in range(self.index_size):
                for k in range(index_np):
                    out_list.append(x_np_reshape[i, j])
        self.out_shape = list(self.x_shape)
        self.out_shape[self.dim] = index_np * self.index_size
        self.out_shape = tuple(self.out_shape)

        out = np.reshape(out_list, self.out_shape)
        self.outputs = {'Out': out}

    def init_dtype_type(self):
        self.dim = 1
        self.x_type = np.float64
        self.x_shape = (8, 4, 5)
        self.index_size = self.x_shape[self.dim]

    def test_check_output(self):
        self.check_output(check_pir=True)

    def test_check_grad_normal(self):
        self.check_grad(['X'], 'Out', check_pir=True)


class TestRepeatInterleaveOpWithOutputSize1(TestRepeatInterleaveOp):
    def setUp(self):
        super().setUp()
        self.attrs['output_size'] = self.out_shape[self.dim]


class TestRepeatInterleaveOpWithOutputSize2(TestRepeatInterleaveOp):
    def setUp(self):
        super().setUp()
        self.attrs['output_size'] = -1


class TestRepeatInterleaveOp2WithOutputSize1(TestRepeatInterleaveOp2):
    def setUp(self):
        super().setUp()
        self.attrs['output_size'] = self.out_shape[self.dim]


class TestRepeatInterleaveOp2WithOutputSize2(TestRepeatInterleaveOp2):
    def setUp(self):
        super().setUp()
        self.attrs['output_size'] = -1


class TestRepeatInterleaveOp_ZeroSize(TestRepeatInterleaveOp2):
    def init_dtype_type(self):
        self.dim = 1
        self.x_type = np.float64
        self.x_shape = (8, 0, 5)
        self.index_size = self.x_shape[self.dim]


class TestIndexSelectAPI(unittest.TestCase):
    def input_data(self):
        self.data_zero_dim_x = np.array(0.5).astype('float32')
        self.data_x = np.array(
            [
                [1.0, 2.0, 3.0, 4.0],
                [5.0, 6.0, 7.0, 8.0],
                [9.0, 10.0, 11.0, 12.0],
            ]
        ).astype('float32')
        self.data_zero_dim_index = np.array(2)
        self.data_index = np.array([0, 1, 2, 1]).astype('int32')
        self.data_index_output_size = np.array([2, 1, 3]).astype('int32')

    def test_repeat_interleave_api(self):
        paddle.enable_static()
        self.input_data()

        # case 1:
        with paddle.static.program_guard(
            paddle.static.Program(), paddle.static.Program()
        ):
            x = paddle.static.data(name='x', shape=[-1, 4], dtype='float32')
            index = paddle.static.data(
                name='repeats_',
                shape=[4],
                dtype='int32',
            )
            if not paddle.framework.in_pir_mode():
                x.desc.set_need_check_feed(False)
                index.desc.set_need_check_feed(False)
            x.stop_gradient = False
            index.stop_gradient = False
            z = paddle.repeat_interleave(x, index, axis=1)
            exe = base.Executor(base.CPUPlace())
            (res,) = exe.run(
                feed={'x': self.data_x, 'repeats_': self.data_index},
                fetch_list=[z],
                return_numpy=False,
            )
        expect_out = np.repeat(self.data_x, self.data_index, axis=1)
        np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)

        # case 2:
        repeats = np.array([1, 2, 1]).astype('int32')
        with paddle.static.program_guard(
            paddle.static.Program(), paddle.static.Program()
        ):
            x = paddle.static.data(name='x', shape=[-1, 4], dtype="float32")
            index = paddle.static.data(
                name='repeats_',
                shape=[3],
                dtype='int32',
            )
            if not paddle.framework.in_pir_mode():
                x.desc.set_need_check_feed(False)
                index.desc.set_need_check_feed(False)
            z = paddle.repeat_interleave(x, index, axis=0)
            exe = base.Executor(base.CPUPlace())
            (res,) = exe.run(
                feed={
                    'x': self.data_x,
                    'repeats_': repeats,
                },
                fetch_list=[z],
                return_numpy=False,
            )
        expect_out = np.repeat(self.data_x, repeats, axis=0)
        np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)

        repeats = 2
        with paddle.static.program_guard(
            paddle.static.Program(), paddle.static.Program()
        ):
            x = paddle.static.data(name='x', shape=[-1, 4], dtype='float32')
            z = paddle.repeat_interleave(x, repeats, axis=0)
            if not paddle.framework.in_pir_mode():
                x.desc.set_need_check_feed(False)
            exe = base.Executor(base.CPUPlace())
            (res,) = exe.run(
                feed={'x': self.data_x}, fetch_list=[z], return_numpy=False
            )
        expect_out = np.repeat(self.data_x, repeats, axis=0)
        np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)

        # case 3 zero_dim:
        if not paddle.framework.in_pir_mode():
            with paddle.static.program_guard(
                paddle.static.Program(), paddle.static.Program()
            ):
                x = paddle.static.data(name='x', shape=[-1], dtype="float32")
                if not paddle.framework.in_pir_mode():
                    x.desc.set_need_check_feed(False)
                z = paddle.repeat_interleave(x, repeats)
                exe = base.Executor(base.CPUPlace())
                (res,) = exe.run(
                    feed={'x': self.data_zero_dim_x},
                    fetch_list=[z],
                    return_numpy=False,
                )
            expect_out = np.repeat(self.data_zero_dim_x, repeats)
            np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)

        # case 4 negative axis:
        with paddle.static.program_guard(
            paddle.static.Program(), paddle.static.Program()
        ):
            x = paddle.static.data(name='x', shape=[-1, 4], dtype='float32')
            index = paddle.static.data(
                name='repeats_',
                shape=[4],
                dtype='int32',
            )

            if not paddle.framework.in_pir_mode():
                x.desc.set_need_check_feed(False)
                index.desc.set_need_check_feed(False)
            z = paddle.repeat_interleave(x, index, axis=-1)
            exe = base.Executor(base.CPUPlace())
            (res,) = exe.run(
                feed={'x': self.data_x, 'repeats_': self.data_index},
                fetch_list=[z],
                return_numpy=False,
            )
        expect_out = np.repeat(self.data_x, self.data_index, axis=-1)
        np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)

        # case 5 output_size:
        with paddle.static.program_guard(
            paddle.static.Program(), paddle.static.Program()
        ):
            x = paddle.static.data(name='x', shape=[-1, 3], dtype='float32')
            index = paddle.static.data(
                name='repeats_',
                shape=[3],
                dtype='int32',
            )
            if not paddle.framework.in_pir_mode():
                x.desc.set_need_check_feed(False)
                index.desc.set_need_check_feed(False)

            z = paddle.repeat_interleave(x, index, axis=1, output_size=6)
            exe = base.Executor(base.CPUPlace())
            (res,) = exe.run(
                feed={
                    'x': self.data_x[:, :3],
                    'repeats_': self.data_index_output_size,
                },
                fetch_list=[z],
            )

        expect_out = np.repeat(
            self.data_x[:, :3], self.data_index_output_size, axis=1
        )
        np.testing.assert_allclose(expect_out, res, rtol=1e-05)

        # case 6 output_size = -1
        with paddle.static.program_guard(
            paddle.static.Program(), paddle.static.Program()
        ):
            x = paddle.static.data(name='x', shape=[-1, 3], dtype='float32')
            index = paddle.static.data(
                name='repeats_',
                shape=[3],
                dtype='int32',
            )
            if not paddle.framework.in_pir_mode():
                x.desc.set_need_check_feed(False)
                index.desc.set_need_check_feed(False)

            z2 = paddle.repeat_interleave(x, index, axis=1, output_size=-1)
            exe = base.Executor(base.CPUPlace())
            (res2,) = exe.run(
                feed={
                    'x': self.data_x[:, :3],
                    'repeats_': self.data_index_output_size,
                },
                fetch_list=[z2],
            )
        np.testing.assert_allclose(expect_out, res2, rtol=1e-05)

        # case 7 output_size error
        with (
            self.assertRaises(ValueError),
            paddle.static.program_guard(
                paddle.static.Program(), paddle.static.Program()
            ),
        ):
            x = paddle.static.data(name='x', shape=[-1, 3], dtype='float32')
            index = paddle.static.data(
                name='repeats_',
                shape=[3],
                dtype='int32',
            )
            z = paddle.repeat_interleave(x, index, axis=1, output_size=5)
            exe = base.Executor(base.CPUPlace())
            exe.run(
                feed={
                    'x': self.data_x[:, :3],
                    'repeats_': self.data_index_output_size,
                },
                fetch_list=[z],
            )

        # case 8 repeats is int, output_size provided and correct
        with paddle.static.program_guard(
            paddle.static.Program(), paddle.static.Program()
        ):
            x = paddle.static.data(name='x', shape=[-1, 3], dtype='float32')
            if not paddle.framework.in_pir_mode():
                x.desc.set_need_check_feed(False)
            z = paddle.repeat_interleave(x, 2, axis=1, output_size=6)
            exe = base.Executor(base.CPUPlace())
            (res3,) = exe.run(
                feed={'x': self.data_x[:, :3]},
                fetch_list=[z],
            )
        expect_out3 = np.repeat(self.data_x[:, :3], 2, axis=1)
        np.testing.assert_allclose(expect_out3, res3, rtol=1e-05)

        # case 9: x.numel = 0, repeats is tensor, output_size = -1
        with paddle.static.program_guard(
            paddle.static.Program(), paddle.static.Program()
        ):
            x = paddle.static.data(name='x', shape=[0, 3], dtype='float32')
            index = paddle.static.data(
                name='repeats_', shape=[3], dtype='int32'
            )
            if not paddle.framework.in_pir_mode():
                x.desc.set_need_check_feed(False)
                index.desc.set_need_check_feed(False)
            z = paddle.repeat_interleave(x, index, axis=1, output_size=-1)
            exe = base.Executor(base.CPUPlace())
            (res4,) = exe.run(
                feed={
                    'x': np.zeros((0, 3), dtype='float32'),
                    'repeats_': self.data_index_output_size,
                },
                fetch_list=[z],
            )
        expect_out4 = np.repeat(
            np.zeros((0, 3), dtype='float32'),
            self.data_index_output_size,
            axis=1,
        )
        np.testing.assert_allclose(expect_out4, res4, rtol=1e-05)

        # case 10: x.numel = 0, repeats is tensor, output_size = actual value
        with paddle.static.program_guard(
            paddle.static.Program(), paddle.static.Program()
        ):
            x = paddle.static.data(name='x', shape=[0, 3], dtype='float32')
            index = paddle.static.data(
                name='repeats_', shape=[3], dtype='int32'
            )
            if not paddle.framework.in_pir_mode():
                x.desc.set_need_check_feed(False)
                index.desc.set_need_check_feed(False)
            output_size_actual = int(self.data_index_output_size.sum())
            z = paddle.repeat_interleave(
                x, index, axis=1, output_size=output_size_actual
            )
            exe = base.Executor(base.CPUPlace())
            (res4b,) = exe.run(
                feed={
                    'x': np.zeros((0, 3), dtype='float32'),
                    'repeats_': self.data_index_output_size,
                },
                fetch_list=[z],
            )
        expect_out4b = np.repeat(
            np.zeros((0, 3), dtype='float32'),
            self.data_index_output_size,
            axis=1,
        )
        np.testing.assert_allclose(expect_out4b, res4b, rtol=1e-05)

        # case 11: repeats tensor dtype = int64, output_size = -1
        with paddle.static.program_guard(
            paddle.static.Program(), paddle.static.Program()
        ):
            x = paddle.static.data(name='x', shape=[-1, 3], dtype='float32')
            index = paddle.static.data(
                name='repeats_', shape=[3], dtype='int64'
            )
            if not paddle.framework.in_pir_mode():
                x.desc.set_need_check_feed(False)
                index.desc.set_need_check_feed(False)
            z = paddle.repeat_interleave(x, index, axis=1, output_size=-1)
            exe = base.Executor(base.CPUPlace())
            (res5,) = exe.run(
                feed={
                    'x': self.data_x[:, :3],
                    'repeats_': self.data_index_output_size.astype('int64'),
                },
                fetch_list=[z],
            )
        expect_out5 = np.repeat(
            self.data_x[:, :3], self.data_index_output_size, axis=1
        )
        np.testing.assert_allclose(expect_out5, res5, rtol=1e-05)

        # case 11: repeats tensor dtype = int64, output_size = actual value
        with paddle.static.program_guard(
            paddle.static.Program(), paddle.static.Program()
        ):
            x = paddle.static.data(name='x', shape=[-1, 3], dtype='float32')
            index = paddle.static.data(
                name='repeats_', shape=[3], dtype='int64'
            )
            if not paddle.framework.in_pir_mode():
                x.desc.set_need_check_feed(False)
                index.desc.set_need_check_feed(False)
            z = paddle.repeat_interleave(x, index, axis=1, output_size=6)
            exe = base.Executor(base.CPUPlace())
            (res6,) = exe.run(
                feed={
                    'x': self.data_x[:, :3],
                    'repeats_': self.data_index_output_size.astype('int64'),
                },
                fetch_list=[z],
            )
        np.testing.assert_allclose(expect_out5, res6, rtol=1e-05)

    def test_dygraph_api(self):
        self.input_data()
        # case axis none
        input_x = np.array([[1, 2, 1], [1, 2, 3]]).astype('int32')
        index_x = np.array([1, 1, 2, 1, 2, 2]).astype('int32')

        with base.dygraph.guard():
            x = paddle.to_tensor(input_x)
            index = paddle.to_tensor(index_x)
            z = paddle.repeat_interleave(x, index, None)
            np_z = z.numpy()
        expect_out = np.repeat(input_x, index_x, axis=None)
        np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)

        # case repeats int
        with base.dygraph.guard():
            x = paddle.to_tensor(input_x)
            index = 2
            z = paddle.repeat_interleave(x, index, None)
            np_z = z.numpy()
        expect_out = np.repeat(input_x, index, axis=None)
        np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)

        # case input dtype is bfloat16
        input_x = np.array([[1, 2, 1], [1, 2, 3]]).astype('uint16')

        with base.dygraph.guard():
            x = paddle.to_tensor(input_x)
            index = paddle.to_tensor(index_x)
            z = paddle.repeat_interleave(x, index, None)
            np_z = z.numpy()
        expect_out = np.repeat(input_x, index_x, axis=None)
        np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)

        with base.dygraph.guard():
            x = paddle.to_tensor(input_x)
            index = 2
            z = paddle.repeat_interleave(x, index, None)
            np_z = z.numpy()
        expect_out = np.repeat(input_x, index, axis=None)
        np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)

        # case 1:
        with base.dygraph.guard():
            x = paddle.to_tensor(self.data_x)
            index = paddle.to_tensor(self.data_index)
            z = paddle.repeat_interleave(x, index, -1)
            np_z = z.numpy()
        expect_out = np.repeat(self.data_x, self.data_index, axis=-1)
        np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)

        with base.dygraph.guard():
            x = paddle.to_tensor(self.data_x)
            index = paddle.to_tensor(self.data_index)
            z = paddle.repeat_interleave(x, index, 1)
            np_z = z.numpy()
        expect_out = np.repeat(self.data_x, self.data_index, axis=1)
        np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)

        # case 2:
        index_x = np.array([1, 2, 1]).astype('int32')
        with base.dygraph.guard():
            x = paddle.to_tensor(self.data_x)
            index = paddle.to_tensor(index_x)
            z = paddle.repeat_interleave(x, index, axis=0)
            np_z = z.numpy()
        expect_out = np.repeat(self.data_x, index, axis=0)
        np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)

        # case 3 zero_dim:
        with base.dygraph.guard():
            x = paddle.to_tensor(self.data_zero_dim_x)
            index = 2
            z = paddle.repeat_interleave(x, index, None)
            np_z = z.numpy()
        expect_out = np.repeat(self.data_zero_dim_x, index, axis=None)
        np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)

        # case 4 zero_dim_index
        with base.dygraph.guard():
            x = paddle.to_tensor(self.data_zero_dim_x)
            index = paddle.to_tensor(self.data_zero_dim_index)
            z = paddle.repeat_interleave(x, index, None)
            np_z = z.numpy()
        expect_out = np.repeat(
            self.data_zero_dim_x, self.data_zero_dim_index, axis=None
        )
        np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)

        # case 5 repeat_interleave_with_tensor_double_grad
        with base.dygraph.guard():
            x_pd = paddle.randn([40, 50])
            x_pd.stop_gradient = False
            axis = 1
            repeats_pd = paddle.randint(1, 50, [x_pd.shape[axis]])

            y_pd = paddle.repeat_interleave(x_pd, repeats_pd, axis)
            dy_pd = paddle.randn_like(y_pd)
            dy_pd.stop_gradient = False
            g_pd = paddle.grad(y_pd, x_pd, dy_pd, create_graph=True)[0]

            ddx_pd = paddle.randn_like(x_pd)
            gg_pd = paddle.grad(g_pd, dy_pd, ddx_pd)[0]

            np.testing.assert_allclose(
                gg_pd.numpy(),
                paddle.repeat_interleave(ddx_pd, repeats_pd, axis).numpy(),
                1e-5,
                1e-5,
            )

        # case 6 repeat_interleave_double_grad
        with base.dygraph.guard():
            x_pd = paddle.randn([40, 50])
            x_pd.stop_gradient = False
            axis = 1
            repeats_pd = 4

            y_pd = paddle.repeat_interleave(x_pd, repeats_pd, axis)
            dy_pd = paddle.randn_like(y_pd)
            dy_pd.stop_gradient = False
            g_pd = paddle.grad(y_pd, x_pd, dy_pd, create_graph=True)[0]

            ddx_pd = paddle.randn_like(x_pd)
            gg_pd = paddle.grad(g_pd, dy_pd, ddx_pd)[0]

            np.testing.assert_allclose(
                gg_pd.numpy(),
                paddle.repeat_interleave(ddx_pd, repeats_pd, axis).numpy(),
                1e-5,
                1e-5,
            )

        # case 7 repeat_interleave_with_i64_tensor_double_grad
        with base.dygraph.guard():
            x_pd = paddle.randn([40, 50])
            x_pd.stop_gradient = False
            axis = 1
            repeats_pd = paddle.randint(
                1, 50, [x_pd.shape[axis]], dtype="int64"
            )

            y_pd = paddle.repeat_interleave(x_pd, repeats_pd, axis)
            dy_pd = paddle.randn_like(y_pd)
            dy_pd.stop_gradient = False
            g_pd = paddle.grad(y_pd, x_pd, dy_pd, create_graph=True)[0]

            ddx_pd = paddle.randn_like(x_pd)
            gg_pd = paddle.grad(g_pd, dy_pd, ddx_pd)[0]

            np.testing.assert_allclose(
                gg_pd.numpy(),
                paddle.repeat_interleave(ddx_pd, repeats_pd, axis).numpy(),
                1e-5,
                1e-5,
            )

        # case 8 0-size_repeat_interleave_with_i64_tensor_double_grad
        with base.dygraph.guard():
            x_pd = paddle.randn([0, 50])
            x_pd.stop_gradient = False
            axis = 1
            repeats_pd = paddle.randint(
                1, 50, [x_pd.shape[axis]], dtype="int64"
            )

            y_pd = paddle.repeat_interleave(x_pd, repeats_pd, axis)
            dy_pd = paddle.randn_like(y_pd)
            dy_pd.stop_gradient = False
            g_pd = paddle.grad(y_pd, x_pd, dy_pd, create_graph=True)[0]

            ddx_pd = paddle.randn_like(x_pd)
            gg_pd = paddle.grad(g_pd, dy_pd, ddx_pd)[0]

            np.testing.assert_allclose(
                gg_pd.numpy(),
                paddle.repeat_interleave(ddx_pd, repeats_pd, axis).numpy(),
                1e-5,
                1e-5,
            )
        # case 9 0-size_repeat_interleave_with_i32_tensor_double_grad
        with base.dygraph.guard():
            x_pd = paddle.randn([0, 50])
            x_pd.stop_gradient = False
            axis = 1
            repeats_pd = paddle.randint(
                1, 50, [x_pd.shape[axis]], dtype="int32"
            )

            y_pd = paddle.repeat_interleave(x_pd, repeats_pd, axis)
            dy_pd = paddle.randn_like(y_pd)
            dy_pd.stop_gradient = False
            g_pd = paddle.grad(y_pd, x_pd, dy_pd, create_graph=True)[0]

            ddx_pd = paddle.randn_like(x_pd)
            gg_pd = paddle.grad(g_pd, dy_pd, ddx_pd)[0]

            np.testing.assert_allclose(
                gg_pd.numpy(),
                paddle.repeat_interleave(ddx_pd, repeats_pd, axis).numpy(),
                1e-5,
                1e-5,
            )

        # case 10 output_size:
        with base.dygraph.guard():
            x = paddle.to_tensor(self.data_x[:, :3])
            index = paddle.to_tensor(self.data_index_output_size)

            z = paddle.repeat_interleave(x, index, axis=1, output_size=6)
            np_z = z.numpy()

        expect_out = np.repeat(
            self.data_x[:, :3], self.data_index_output_size, axis=1
        )
        np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)

        with base.dygraph.guard():
            x = paddle.to_tensor(self.data_x[:, :3])
            index = paddle.to_tensor(self.data_index_output_size)

            z = x.repeat_interleave(index, axis=1, output_size=6)
            np_z = z.numpy()

        np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)

        with base.dygraph.guard():
            x_np = np.array([[1.0, 2.0], [3.0, 4.0]]).astype('float32')
            index_np = np.array([2, 1]).astype('int32')

            x = paddle.to_tensor(x_np, stop_gradient=False)
            index = paddle.to_tensor(index_np)
            z = paddle.repeat_interleave(x, index, axis=1, output_size=3)

            z.backward()

            expected_grad = np.array([[2.0, 1.0], [2.0, 1.0]])
            np.testing.assert_allclose(
                x.grad.numpy(), expected_grad, rtol=1e-05
            )

            x = paddle.to_tensor(x_np, stop_gradient=False)
            z = x.repeat_interleave(index, axis=1, output_size=3)

            z.backward()

            np.testing.assert_allclose(
                x.grad.numpy(), expected_grad, rtol=1e-05
            )

        with base.dygraph.guard():
            x = paddle.to_tensor(self.data_x[:, :3])
            index = paddle.to_tensor(self.data_index_output_size)

            with self.assertRaises(ValueError):
                z = paddle.repeat_interleave(x, index, axis=1, output_size=5)


if __name__ == '__main__':
    unittest.main()
